Despite recent progress, there is still uncertainty about the pathophysiology of movement disorders in man. However there is an awareness that neurologic disease affects motor and cognitive processes by altering neural networks in functionally specific ways. Recently, covariance analysis has been applied to the analysis of Positron Emission Tomography (PET) images of brain glucose metabolism, and has identified some of the underlying abnormal circuitry in idiopathic torsion dystonia and Parkinson's disease. Yet there studies require fairly large numbers of patients and normal controls. Furthermore they do not definitively identify the primary locus of metabolic abnormality, since the abnormal profiles may be an epiphenomenon reflecting maintenance of the dystonic posture. A way of increasing sensitivity is to use the fact that in most movement disorders the abnormal movements disappear in sleep. Sleep is therefore a useful internal reference state uncontaminated by movement. Brain regions showing focal differences between wake and sleep are therefore associated with the anatomic substrate of the disorder. This approach has been validated in a study of craniocervical dystonia (Meige syndrome). Yet is does not reveal the functional interactions of these brain regions. It is therefore proposed to combine the two approaches, in order to obtain a sensitive measure of the underlying neural circuitry. Four hyperkinetic disorders will be studied using regional measures of cerebral glucose metabolism and blood flow: blepharospasm, idiopathic torsion dystonia, Huntington's chorea and levodopa-induced dyskinesia. In addition, covariance analysis of sleeping patients and sleeping normals is expected, finally, to reveal the primary metabolic substrate. Validation of this approach will mediate between existing theories of cortical-subcortical interactions, and may suggest possible surgical or pharmacological intervention in these disorders.